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Glossary

Neural Network

What is a neural network?

A neural network (NN) is a computing system loosely inspired by the structure and function of the human brain.

It consists of interconnected nodes, called artificial neurons or units, organized in multiple layers. These neurons are inspired by the biological neurons found in the brain.

Neural networks are widely used in the field of machine learning. They provide a framework for various algorithms to process and analyze complex data. By using a neural network, algorithms can learn to perform tasks such as classification, regression, pattern recognition, and more.

The learning process in a neural network involves analyzing examples or data inputs and adjusting the strength of connections between neurons, known as weights. This adjustment is based on the patterns and relationships found in the input data, allowing the neural network to recognize and generalize from these patterns. This ability to “learn” from data without explicit instructions is known as “learning from examples” or “learning from data”.

Why neural networks?

Neural networks, a component of machine learning, can be used to solve complex signal processing or pattern recognition problems. They have demonstrated remarkable performance in various domains, including computer vision, natural language processing, speech recognition, and many others.

Commercial applications of neural networks include pattern recognition and forecasting. They’ve been used to recognize handwriting for check processing, transcribe speech to text, predict the weather, predict stock market fluctuations, recognize facial features, and plan and optimize delivery routes.

Our team has worked on different case studies for neural networks:

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